Examining Canadian Student-Athlete’s Lived Experiences During the COVID-19 Pandemic: Considerations for Adolescents During Times of Sport Disruption.
Bibliographic record
Abstract
The COVID-19 pandemic brought unprecedented challenges to Canadian Adolescent Student-Athletes (CASAs), disrupting their sporting routines, educational experiences, and social connections. This study delves into how CASAs navigated this period of uncertainty, examining the impact on their athletic identities and overall well-being. Using Interpretive Description Methodology, I conducted semi-structured interviews with twelve adolescents aged 15-19 years, competing at provincial, national, or international levels across various sports. My analysis, guided by an integrated theoretical framework combining Narrative Identity Theory, Ecological Systems Theory, and the Assumptive World Theory of Trauma, revealed significant disruptions to participants' sense of self and purpose. CASAs grappled with feelings of emptiness and loss of motivation without their usual sporting engagements. However, many demonstrated remarkable resilience, finding new ways to maintain connections to their athletic identities and adapt to the changing circumstances. ii The study uncovered various coping strategies employed by CASAs, including shifting goal orientations, exploring new interests, and leaning on social support from peers, family, and coaches. Importantly, the research highlighted the complex interplay between athletic identity, academic pursuits, and social connections in shaping CASAs' experiences during the pandemic. Drawing on these findings, I propose the Athlete Identity Resilience Model (AIRM), which synthesizes elements from the theoretical framework to explain how adolescent athletes maintain, reconstruct, or expand their identities during significant disruptions. This model provides a framework for understanding the processes of identity maintenance, destabilization, and potential growth experienced by CASAs during the pandemic. This study contributes to our understanding of athletic identity development in times of crisis and offers insights for developing more effective support systems for young athletes facing future disruptions. The findings underscore the need for holistic approaches to supporting CASAs, considering not only their athletic development but also their psychological well-being and social connections.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".